Causal Modeling-Based Discrimination Discovery and Removal: Criteria, Bounds, and Algorithms

By Lu Zhang, Yongkai Wu, Xintao Wu. 2019

54 citations recorded by the source corpus.

Anti-discrimination is an increasingly important task in data science. In this paper, we investigate the problem of discovering both direct and indirect discrimination from the historical data, and removing the discriminatory effects before the data are used for predictive analysis (e.g., building classifiers). The main drawback of existing methods is that they cannot distinguish the part of influence that is really caused by discrimination from all correlated influences. In our approach, we make use of the causal graph to capture the causal structure of the data. Then, we model direct and indirect discrimination as the path-specific effects, which accurately identify the two types of discrimination as the causal effects transmitted along different paths in the graph. For certain situations where indirect discrimination cannot be exactly measured due to the unidentifiability of some path-specific effects, we develop an upper bound and a lower bound to the effect of indirect discrimination. Based on the theoretical results, we propose effective algorithms for discovering direct and indirect discrimination, as well as algorithms for precisely removing both types of discrimination while retaining good data utility. Experiments using the real dataset show the effectiveness of our approaches.

Subfields: Artificial Intelligence, Statistics and Probability, Causal Inference & Discovery, AI Ethics, Fairness & Society

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Citations in the corpus

A Survey on Bias and Fairness in Machine Learning (2019, methodology): “n [13] [1] Machine learning PCA [111] Machine learning Community detection [85] Machine learning Clustering [30] [7] Machine learning Graph embedding [21] Machine learning Causal inference [81] [136] [137] [132] [95] [94] [134] [69] [105] [133] Natural language processing Word embedding [19] [141] [”
Counterfactual Fairness: Unidentification, Bound and Algorithm (2019, background): “In recent years, the research community has studied fairness-aware machine learning from the causal perspective [Zhang et al., 2017b; Zhang et al., 2017a; Zhang and Bareinboim, 2018; Nabi and Shpitser, 2018; Zhang et al., 2018b; Zhang et al., 2018a] using causal modeling [Pearl, 2009].”
PC-Fairness: A Unified Framework for Measuring Causality-based Fairness (2019, methodology): “In [20] the authors propose a method to bound indirect discrimination as the path-speciﬁc effect in unidentiﬁable situations, and in [14] a method is proposed to bound counterfactual fairness.”